By Roman Kozhan
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Extra info for Financial Econometrics
1). In EViews, the Nonlinear Least Squares method has the same implementation as the OLS. The only difference os that the model in the Equation specification box should be entered as a mathematical expression instead of a list of variables. for example, y=@exp(c(1)*x)+(c(2)*z+4)∧ 2 Please click the advert Interpretation of the estimation output, residual diagnostic and inference can be performed in the same way as for the OLS regression. 1 Introduction Time series is a sequence of numerical data in which observations are measured at a particular instant of time.
A consistent estimator of the asymptotic variance of the parameters estimator is var βˆ = σ ˆ 2 (X X) . 3). , where Goodness of fit is summarized by the R2 of the regression R2 = 1 − RSS T SS n T SS = i=1 Yi − Y¯ 2 . The coefficient R2 measures the percentage of the variation of the dependent variable Y that is explained by the variation of the regressors X. The usual R2 has the undesirable feature of never decreasing as more variables are added to the regression, even if the extra variables are irrelevant.
Values of DW around 2 indicate no serial correlation in the error terms, values less than 2 suggest positive serial correlation, and values greater than 2 suggest negative serial correlation. Exact critical values for a general case cannot be tabulated; however, Durbin and Watson (1950) established upper and lower bounds (dU and dL respectively) for the critical values. The testing procedure is as follows: • if DW < dL we reject the null hypothesis of no autocorrelation in favour of positive first-order autocorrelation; • if DW > dU we do not reject the null hypothesis The bounds for critical values in the case of negative autocorrelation alternative are 4 − dU and 4 − dL .